For the last 10,000 years, crops have been cultivated on the basis of unsystematically collected data. Now that humankind has harnessed electromagnetic radiation that penetrates vegetation, more systematic assessment of the condition of crops in vivo should have become possible, however hurdles described below have precluded their application to date.
Precision agriculture describes a management technique based on crop and soil data measured in the field as a function of position and time. The correlation of data with its position in the field over time is used to make farm management decisions that can maximize overall returns. Data collection can include information about four main areas: farm environment, soil, plants or the final crops. Much value, in terms of crop yield and quality would be attainable if there were only some way to provide specific and accurate data for a number of these key areas across a wide variety of different crops, especially in the area of specialty crops. Specialty crops include high value fruits, vegetables and nuts which are used for food or medicine which are marketed directly to customers and thus require exceptionally high esthetic quality. For these crops, a high value is placed both on accurate yield prediction as well as plant health. Early and accurate measurement of crop yields enables staging of equipment and resources for harvest, packaging and storage as well as the capability to accurately price crops. For tree and vine based specialty crops, the maintenance and health of the plant year over year is also critical. Collection of yield data and plant health year after year would enable predictive tools to be used for fertilization and watering as well as harvest planning.
Crop yield estimation is an important task in the management of a variety of agricultural crops, including fruit orchards such as apples. Fruit crops, such as apples, citrus fruits, grapes, and others, are composed of the plant, which is starch rich and relatively low density (leaves, branches, and stems), as well as the fruit, which is compact and water saturated. Current techniques for estimation rely on statistical sampling using humans to provide yield estimation, which is time-consuming, labor intensive, and inaccurate.
According to Wang, et al., “Automated Crop Yield Estimation for Apple Orchards,” 13th International Symposium on Experimental Robotics (ISER 2012), which is incorporated herein by reference,
Accurate yield prediction helps growers improve fruit quality and reduce operating costs by making better decisions on the intensity of fruit thinning and size of the harvest labor force. It benefits the packing industry as well, because managers can use estimation results to optimize packing and storing capacity. Typical yield estimation is performed based on historical data, weather conditions, and workers manually counting yield of fruit, such as apples, in multiple sampling locations. This process is time-consuming and labor intensive, and the limited sample size is usually not enough to reflect the yield distribution across the orchard, especially in those with high spatial variability. Therefore, the current yield estimation practice is inaccurate and inefficient, and improving current practices would be a significant result to the industry. Ibid, p. 1.
While x-ray scattering had been observed for some time, the mechanism whereby x-ray quanta are scattered by electrons was first described by Compton, “On the Mechanism of X-Ray Scattering,” Proc. Nat. Acad. Sci., vol. 11, pp. 303-06 (1925), incorporated herein by reference, and has since been referred to as “Compton scattering.” Prior suggestions to use x-ray backscatter in characterizing plant material have been limited to applications in which agricultural produce has already been picked and is being handled under controlled conditions. These include actual handling of food during processing, as discussed by Cruvinel et al., “Compton Scatter Tomography for Agricultural Measurements,” Eng. Aric. Jaboticabal, vol. 26, pp. 151-60 (2006), and U.S. Pat. No. 7,734,012 (to Boyden et al.), both of which are incorporated herein by reference.
Fruit growers desire an automated system for conducting crop yield estimates. Current techniques focus on visual imaging systems. Estimation based on the visual environment is challenging due to variable illumination, occlusion due to foliage, and multiple counts. Occlusion by foliage may result in multiple counts, based on difficulty viewing the entire crop. Visual imaging processing may advantageously also be computationally intensive.
Application of x-ray backscatter to crops that have not been harvested and that are alive in the field offers particular benefits, but would require specialized techniques not hitherto known in the art. Those specialized techniques and benefits are described in detail below, in accordance with the present invention. For purposes of the present description, plant matter shall be said to be “living” if it is consuming energy in a process of growth.